Search results

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.

In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.

The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.

COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.

We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.

Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.

This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

The main goal of the article is to determine the mediating role of human resources management (HRM) outcomes in the relationships between shaping employee work engagement and job satisfaction (SEWE&JS) and company performance results and to establish whether there are any identifiable regularities in this scope in the pre-pandemic and pandemic period in the headquarters (HQs) and foreign subsidiaries of multinational companies (MNCs).

The empirical research included 200 MNCs headquartered in Central Europe. The raw data in the variables were adjusted with the efficiency index (EI) to capture the actual relations between the variables under study. The partial least squares structural equation modeling (PLS-SEM) was used to verify the research hypotheses and assess the mediating effects.

The research findings show that the HRM outcomes positively mediate the relationships between SEWE&JS and the company performance results. HRM outcomes turned out to be a stronger mediator between SEWE&JS and company performance results in finance and quality in the HQs during the pandemic. By contrast, in the local subsidiaries, they were a stronger mediator of the relationships between the results in innovativeness and quality during the pandemic.

In addition to confirming the results of some other researchers, the research findings also provide new knowledge. They determine the mediating role of HRM outcomes in the relationship between SEWE&JS and the three categories of company performance results, namely finance, innovativeness and quality. In addition, they identify certain regularities in the four studied contexts, which is a novelty in this type of research. A novelty is also the use of employee key performance indicators (KPIs) in the data analysis as the efficiency index in analyzing the effect of the variables under study. The value of the research is also the fact that it covers HRM in MNCs established in Central Europe, which, compared to MNCs from the Western world, is not a frequent subject of research.

In order to better optimize the internal management system of book publishing and to cope with the changes in the external market environment, the purpose of this paper is to carry out cross-border publishing with the help of a transmedia storytelling model to realize the transformation and upgrading of the industry. Focusing on the relationship between the book publishing transmedia storytelling model and business performance, the moderating effect of the innovation environment on different variables is assessed.

This paper proposes several feasible hypotheses based on existing research. The research data came from 365 managers of Chinese book publishing organizations, and the scale was validated by Cronbach’s a, composite reliability (CR) and average variance extracted (AVE). Reliability and validity were verified, and correlation and regression analyses were used to test the impact of the book publishing transmedia storytelling model on business performance and to analyze the moderating role of the innovation environment.

The results show that the book publishing transmedia storytelling model (content production, technology integration, organizational innovation, marketing integration) helps to improve business performance (market performance, financial performance), and the innovation environment has a positive moderating effect on the relationship between the book publishing transmedia storytelling model and business performance, which provides a guarantee for the transformation and upgrading of book publishing. The market information reflected in the innovation environment has a certain role in promoting the innovation and business performance of the book publishing transmedia storytelling model.

The empirical evidence provides a theoretical link between the book publishing transmedia storytelling model and business performance, but there are still some shortcomings, and more factors, such as equity structure, government subsidies and research and development investment, should be included in future research. In addition, the scope of the research should be broadened on this basis to make the results of the data analysis more objective.

This paper introduces the transmedia storytelling model and deeply analyzes the relationship between the book publishing transmedia storytelling model and business performance, which is of great practical significance for optimizing the application and service quality of book publishing, prolonging the industrial chain, enhancing the interaction and participation of users and perfecting the business management system of the book publishing industry.

The application and research of the book publishing transmedia storytelling model are imperfect. Therefore, this paper not only helps to promote the innovation of book publishing organizational structure and improve the management system of business performance, but also may help to improve the innovation environment of book publishing enterprises and promote the diversification of industrial structure.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.